Compression of power system signals

ABSTRACT

The present disclosure pertains to systems and methods to compress an input signal representing a parameter in an electric power system. In one embodiment, a system includes a data acquisition subsystem to receive an input signal comprising a plurality of high-speed representations of electrical conditions. A linear prediction subsystem generates an excitation signal estimate based on the input signal, a plurality of linear prediction coefficients based on the input signal, and an estimated signal based on the excitation signal estimate and the plurality of linear prediction coefficients. An error encoding subsystem may generate an encoding of an error signal based on a difference between the input signal and the estimated signal. A non-transitory computer-readable storage medium may store an encoded and compressed representation of the input signal comprising the excitation signal estimate, the plurality of linear prediction coefficients, and the encoding of the error signal.

TECHNICAL FIELD

The present disclosure pertains to encoding and compressing power systemsignals using linear prediction and an encoding of an error signal. Moreparticularly, but not exclusively, the systems and methods disclosedherein may be used to compress data representing time-domainmeasurements of electrical parameters for archival and/or transmission.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the disclosure aredescribed, including various embodiments of the disclosure withreference to the figures, in which:

FIG. 1 illustrates a simplified one-line diagram of an electric powerdelivery system consistent with embodiments of the present disclosure.

FIG. 2A illustrates a functional block diagram of a system to encode andcompress power system signals using linear prediction and Golomb codesfor storage on a computer-readable storage medium consistent withembodiments of the present disclosure.

FIG. 2B illustrates a functional block diagram of a system to encode andcompress power system signals using linear prediction and an encoding ofan error signal for real-time encoding and transmission consistent withembodiments of the present disclosure.

FIG. 3 illustrates a functional block diagram of a system to decode anddecompress power system signals using linear prediction and Golomb codesconsistent with embodiments of the present disclosure.

FIG. 4 illustrates a block diagram of a system to encode and decodecompressed power system signals using linear prediction and an encodingof an error signal consistent with embodiments of the presentdisclosure.

FIG. 5 illustrates a flow chart of a method to encode power systemsignals using linear prediction and Golomb codes consistent withembodiments of the present disclosure.

DETAILED DESCRIPTION

High-speed monitoring of electric power systems provides improved powersystem stability because if faults are not cleared before a criticalfault clearing time, the system may lose transient stability andpossibly suffer a blackout. In addition, faster fault clearing increasesthe amount of power that can be transferred through a given system.Faster protection also enhances public and utility personnel safety,limits equipment wear, improves power quality, and reduces propertydamage.

High-speed protection devices respond to high-frequency signalcomponents, which may be used to detect faults and to realize variousadvantages. For example, wind and solar power sources are connected tothe power system through a power electronics interface and typicallyhave little or no inertia. Their control algorithms protect theconverters from fault conditions. As a result, these sources producevoltages and currents that challenge some protection principlesdeveloped for networks with synchronous generators. In contrast,high-speed protection devices configured to respond to high-frequencysignal components are less dependent on the sources and more dependenton conditions in the electric power system. As a result, such relays maybe useful in applications near nontraditional sources.

Further, high-speed monitoring may enable analysis of traveling waves(TWs) to aid in the detection of faults. When a fault occurs in anelectric power system, traveling waves are launched from the fault andtravel outward at a velocity near the speed of light. The travelingwaves are reflected by buses and other discontinuities according totheir corresponding characteristic impedances. In the initial stage ofthe fault, the electric power system may behave like a distributedparameter network. Accordingly, the traveling waves may be described bya propagation velocity, reflection and transmission coefficients, and aline characteristic impedance. Using a traveling wave detectionalgorithm, a high-speed relay may be able to detect a fault and initiatecorrective action in less than 1 millisecond.

High-speed monitoring generates large quantities of data. The data canprovide valuable information about the operation of electric powersystems over time; however, the volume of data can make storage andtransmission difficult. Devices that store such information may belocated in substations distributed throughout an electric power systemand may have limited connectivity due to security concerns.

The inventors of the present disclosure have recognized that certainadvantages may be realized by utilizing linear prediction and anencoding of an error signal for compression of power system signals.Various embodiments consistent with the present disclosure may providelossless compression of power system signals for storage, and therefore,may allow more information to be stored in a device of a given capacity.Further, various embodiments may compress information for transmission,thus allowing such data to be communicated more quickly and/orcommunicated over lower bandwidth channels.

As used herein, an IED may refer to any microprocessor-based device thatmonitors, controls, automates, and/or protects monitored equipmentwithin a system. Such devices may include, for example, differentialrelays, distance relays, directional relays, feeder relays, overcurrentrelays, voltage regulator controls, voltage relays, breaker failurerelays, generator relays, motor relays, remote terminal units,automation controllers, bay controllers, meters, recloser controls,communication processors, computing platforms, programmable logiccontrollers (PLCs), programmable automation controllers, input andoutput modules, and the like. The term IED may be used to describe anindividual IED or a system comprising multiple IEDs. Further, IEDs mayinclude sensors (e.g., voltage transformers, current transformers,contact sensors, status sensors, light sensors, tension sensors, etc.)that provide information about the electric power system.

The embodiments of the disclosure will be best understood by referenceto the drawings. It will be readily understood that the components ofthe disclosed embodiments, as generally described and illustrated in thefigures herein, could be arranged and designed in a wide variety ofdifferent configurations. Thus, the following detailed description ofthe embodiments of the systems and methods of the disclosure is notintended to limit the scope of the disclosure, as claimed, but is merelyrepresentative of possible embodiments of the disclosure. In addition,the steps of a method do not necessarily need to be executed in anyspecific order, or even sequentially, nor do the steps need to beexecuted only once, unless otherwise specified.

In some cases, well-known features, structures, or operations are notshown or described in detail. Furthermore, the described features,structures, or operations may be combined in any suitable manner in oneor more embodiments. It will also be readily understood that thecomponents of the embodiments, as generally described and illustrated inthe figures herein, could be arranged and designed in a wide variety ofdifferent configurations. For example, throughout this specification,any reference to “one embodiment,” “an embodiment,” or “the embodiment”means that a particular feature, structure, or characteristic describedin connection with that embodiment is included in at least oneembodiment. Thus, the quoted phrases, or variations thereof, as recitedthroughout this specification are not necessarily all referring to thesame embodiment.

Several aspects of the embodiments disclosed herein may be implementedas software modules or components. As used herein, a software module orcomponent may include any type of computer instruction orcomputer-executable code located within a memory device that is operablein conjunction with appropriate hardware to implement the programmedinstructions. A software module or component may, for instance, compriseone or more physical or logical blocks of computer instructions, whichmay be organized as a routine, program, object, component, datastructure, etc., that performs one or more tasks or implementsparticular abstract data types.

In certain embodiments, a particular software module or component maycomprise disparate instructions stored in different locations of amemory device, which together implement the described functionality ofthe module. A module or component may comprise a single instruction ormany instructions and may be distributed over several different codesegments, among different programs, and across several memory devices.Some embodiments may be practiced in a distributed computing environmentwhere tasks are performed by a remote processing device linked through acommunications network. In a distributed computing environment, softwaremodules or components may be located in local and/or remote memorystorage devices. In addition, data being tied or rendered together in adatabase record may be resident in the same memory device, or acrossseveral memory devices, and may be linked together in fields of a recordin a database across a network.

Embodiments may be provided as a computer program product including anon-transitory machine-readable medium having stored thereoninstructions that may be used to program a computer or other electronicdevice to perform processes described herein. The non-transitorymachine-readable medium may include, but is not limited to, hard drives,floppy diskettes, optical disks, CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs,EEPROMs, magnetic or optical cards, solid-state memory devices, or othertypes of media/machine-readable media suitable for storing electronicinstructions. In some embodiments, the computer or another electronicdevice may include a processing device such as a microprocessor,microcontroller, logic circuitry, or the like. The processing device mayfurther include one or more special-purpose processing devices such asan application-specific interface circuit (ASIC), PAL, PLA, PLD,field-programmable gate array (FPGA), or any other customizable orprogrammable device.

FIG. 1 illustrates a block diagram of a system 100 for monitoring atransmission line 106 in an electric power system consistent withembodiments of the present disclosure. Transmission line 106 connectstwo nodes in the electric power system, which are illustrated as a firstterminal 112 and a second terminal 114. First and second terminals 112and 114 may be buses in a transmission system supplied by generators 116and 118, respectively. Although illustrated in single-line form forpurposes of simplicity, system 100 may be a multi-phase system, such asa three-phase electric power system.

System 100 is monitored by IEDs 102 and 104 at two locations of thesystem, although further IEDs may also be utilized to monitor additionallocations of the system. IEDs 102 and 104 may obtain electric powersystem information using current transformers (CTs), potentialtransformers (PTs), Rogowski coils, voltage dividers, and/or the like.IEDs 102, 104 may be capable of using inputs from conventionalinstrument transformers such as CTs and PTs conventionally used in themonitoring of electric power delivery. IEDs 102 and 104 may also receivecommon time information from a common time source 110. In one specificembodiment, IEDs 102 and 104 may be embodied as SEL-T400L Time DomainLine Protection systems available from Schweitzer EngineeringLaboratories (“SEL”) of Pullman, Washington.

Common time source 110 may be any time source capable of delivering acommon time signal to each of IEDs 102 and 104. Some examples of acommon time source include a Global Navigational Satellite System (GNSS)such as the Global Positioning System (GPS) delivering a time signalcorresponding with IRIG, a WWVB or WWV system, a network-based systemsuch as corresponding with IEEE 1588 precision time protocol, and/or thelike. According to one embodiment, common time source 110 may comprise asatellite-synchronized clock (e.g., Model No. SEL-2407, available fromSEL). Further, it should be noted that each IED 102, 104 may be incommunication with a separate clock, such as a satellite-synchronizedclock, with each clock providing each IED 102, 104 with a common timesignal. The common time signal may be derived from a GNSS system orother time signal.

A data communication channel 108 may allow IEDs 102 and 104 to exchangeinformation relating to, among other things, voltages, currents,time-domain fault detection, and location. According to someembodiments, a time signal based on common time source 110 may bedistributed to and/or between IEDs 102 and 104 using data communicationchannel 108. Data communication channel 108 may be embodied in a varietyof media and may utilize a variety of communication protocols. Forexample, data communication channel 108 may be embodied utilizingphysical media, such as coaxial cable, twisted pair, fiber optic, etc.Further, data communication channel 108 may utilize communicationprotocols such as Ethernet, SONET, SDH, or the like to communicate data.

IEDs 102 and 104 may provide various types of monitoring and protectionby monitoring various electrical conditions in the time-domain, such astraveling wave detection, overcurrent protection, differentialprotection, and the like. IEDs 102 and 104 may measure electricalparameters at high speeds (e.g., 1 million samples per second). Themeasured electrical parameters may be stored for various purposes, suchas post-event analysis, system analysis, event playback, testing, etc.Compressing the data generated by IEDs 102 and 104 may allow for agreater quantity of data to be stored in a given data storage device andmay allow for such data to be more readily communicated for off-site useor analysis.

FIG. 2A illustrates a functional block diagram of a system 200 to encodeand compress power system signals using linear prediction and Golombcodes consistent with embodiments of the present disclosure. In onespecific embodiment, IEDs 102 and 104 illustrated in FIG. 1, maycomprise system 200 and may encode and compress data collected from theelectric power system 100.

System 200 receives an input signal to be encoded and compressed.Although various embodiments specifically refer to electric power systemdata, other types of data may also be encoded and compressed by system200. A linear prediction estimator 202 may receive the input signal,x(n), and generate a plurality of linear prediction coefficients, a_(i).The linear prediction coefficients may be used to generate an estimatedsignal, {circumflex over (x)}(n), using a linear function of previoussamples, according to Eq. 1.

{circumflex over (x)}(n)=Σ_(i=1) ^(p) a _(i) x(n−i)  Eq. 1

The linear prediction coefficients are provided to a linear predictor204 and a non-transitory computer-readable storage medium 206. Eq. 1models the system as an all-pole filter and models the input signal aseither a pitched (e.g., periodic) or unpitched (e.g., non-periodic, orthe output of a stochastic system) signal.

Linear prediction estimator 202 may also generate an excitation signalestimate. The excitation signal estimate is typically determined byautocorrelation, although other frequency estimate techniques may beused. If the autocorrelation coefficients are of sufficient magnitude,the excitation signal is modeled as pitched, and estimates of magnitude,frequency, and phase may be calculated and stored; otherwise, it ismodeled as unpitched, and variance and magnitude may be estimated andstored. The excitation signal estimate represents an abstract input tothe model, used to regenerate an estimate of the input signal in a morecompact representation.

Various strategies may be selected in different embodiments to optimizethe value of a_(i). For example, the root mean square or autocorrelationcriteria may be used to minimize the value of the squared error. Otherstrategies, such as robust regressions (e.g., Student's t, Poisson,etc.) may also be utilized if the input signal is found to be non-normalor to contain significant outliers.

The linear predictor 204 may use Eq. 1 to generate an estimated signal.The estimated signal may not fully represent the input signal. Thedifferences between the input signal and the estimated signal may bedetermined by summer 208, which generates an error signal, e(n), basedon the difference between the input signal and the estimated signal, asexpressed in Eq. 2.

e(n)=x(n)−{circumflex over (x)}(n)  Eq. 2

Capturing the error signal may allow for the estimated signal to becorrected to accurately represent the input signal. In a high-speedmonitoring system, the error signal is likely to be close to zero. Thefunction of summing the estimated signal and error signal may beperformed by a general-purpose processor, a digital signal processor, orthe like.

The error signal may be provided to Golomb coder 210. Golomb codingprovides efficient coding of an input stream in which small values aremore likely than large values. Golomb coding uses a tunable parameter,M, to divide an input value, N, into two parts: the quotient, q, and theremainder, r. In some embodiments, the quotient, q, may be encoded inunary coding, while the remainder, r, may be encoded using truncatedbinary coding. In other embodiments, including the specific embodimentsdescribed below, a variety of schemes may be used to encode the errorsignal.

Unary encoding represents a natural number, n, as a series of onesfollowed by a 0 (e.g., 0 is represented as 0, 1 is represented as 10, 2is represented as 110, and 3 is represented as 1110) or a series ofzeros followed by a 1 (e.g., 0 is represented as a 1, 1 is representedas 01, 2 is represented as 001, and 3 is represented as 0001). So longas the average number of bits used in unary encoding is less than thelength of the machine representation natively, compression is achieved.For instance, for eight-bit values, if the average of the values beingrepresented is less than seven, which requires eight bits in unaryencoding, the values can be more compactly represented.

Truncated binary encoding more compactly represents a value x in analphabet of size n, where 0≤x≤n. Let k=floor(log₂(n)) such that 2^(k)<n<2 ^(k+1), and let u=2^(k+1)−n. Then, assign the first u symbolsto codewords of length k and the remaining n−u symbols to codewords oflength k+1. If n is a power of two, truncated binary encoding isidentical to normal binary encoding. For example, if n=5, then k=2 andu=3 and the truncated binary encoding is as shown below.

Truncated Binary Encoding 0

0 0 1

0 1 2

1 0 NA

NA

NA

3 1 1 0 4 1 1 1Values with a strike-through are not used in truncated binary encoding.Therefore, the value 0 is encoded as 00, 1 is encoded as 01, 2 isencoded as 10, 3 is encoded as 110, and 4 as 111.

System 200 may create an encoded and compressed representation of theinput signal that requires significantly less space to store oncomputer-readable storage medium 206. In one embodiment, the inputsignal may comprise a one million samples per second signal in whicheach sample is represented by 18-bits of information. The encoded andcompressed representation may represent the same information usingbetween approximately 4 and 6 bits per sample. In other embodiments, acompression ratio (i.e., the size of the uncompressed data divided bythe size of the compressed data) may be between 4 and 8.

FIG. 2B illustrates a functional block diagram of a system 250 to encodeand compress power system signals using linear prediction and anencoding of the error signal for real-time encoding and transmissionconsistent with embodiments of the present disclosure. In real-timetransmission, encoding of the input signal is a limiting factor, and assuch, various techniques may be employed to increase encoding speed.

System 250 includes a linear predictor 252 that generates an estimatedsignal. In one specific embodiment, linear predictor 252 may be embodiedas a fixed coefficient second-order linear predictor. Linear predictor252 may generate an estimated signal that is provided to a communicationinterface 258 and a summer 254. In contrast to system 200 illustrated inFIG. 2A, system 250 does not include a linear prediction estimator thatgenerates linear prediction coefficients; however, other embodiments mayinclude a dynamic estimator or other device to generate an estimatedsignal.

An error signal may be generated by summer 254 and encoded by differenceencoder 256. The estimated signal generated by linear predictor 252 maybe compared to the input signal by summer 254 to generate the errorsignal. The error signal may be provided to difference encoder 256.Difference encoder 256 may use a variety of techniques to generate anencoded error signal. In one specific embodiment, the difference encoder256 may use a “zigzag” encoding that maps negative numbers to positivenumbers, as shown in the following table. As illustrated, the encodedvalue “zigzags” between negative and positive values.

Signed Value Encoded Value 0 0 −1 1 1 2 −2 3

In another embodiment, a variable-length encoding scheme that mapsintegers into sequences of bytes of variable length may be used. If thevalue of the error signal is small, the average length of the integervalue is less than a direct binary encoding. For example, for 18-bitdata, the differences between the variable-length encoding and theactual signal could be as large as ±2¹⁷ or ±131,072. If the averageerror signal value is less than ±128, the average variable-lengthrepresentation would be 8 bits, which would represent a considerablereduction in comparison to a signal represented by a fixed lengthencoding (e.g., an 18-bit signal).

In yet another example, the difference encoder 256 may map integervalues into a sequence of smaller pieces, referred to as “nibbles.” Ifthe average error value is less than ±15, the encoded error signal canbe represented in “nibbles” of 4 bits. For larger error values, multiple“nibbles” may be used.

Communication interface 258 may receive the estimated signal from linearpredictor 252 and the encoding of the error signal from differenceencoder 256 to transmit a representation of the input signal.Communication interface 258 may be embodied by a variety ofcommunication technologies. In one specific embodiment, system 250 maybe embodied by an IED, and the input signal may represent electricalparameters to be streamed to a remote computer system.

FIG. 3 illustrates a functional block diagram of a system 300 to decodeand decompress power system signals stored using linear prediction andGolomb codes consistent with embodiments of the present disclosure. Asignal to be decoded and decompressed is stored in non-transitorycomputer-readable storage medium 302. In one embodiment, the signal tobe decoded may have been encoded and compressed by system 200illustrated in FIG. 2A. In other embodiments, the signal to be decodedmay be received from a communication interface receiving a streamingsignal encoded by system 250 illustrated in FIG. 2B.

Non-transitory computer-readable storage medium 302 may include anexcitation signal estimate, linear prediction coefficients, and Golombcodes. The excitation signal estimate and linear prediction coefficientsmay be provided to linear predictor 304. The excitation signal estimateand linear prediction coefficients may be used to generate an estimatedsignal that is provided to summer 308.

Non-transitory computer-readable storage medium 302 may also includeGolomb codes representing an error signal. The error signal may begenerated by Golomb decoder 306 and provided to summer 308. The sum ofthe estimated signal and the error signal may represent the originalsignal.

FIG. 4 illustrates a functional block diagram of a system 400 tocompress and decompress power system signals using linear prediction andGolomb codes consistent with embodiments of the present disclosure.System 400 may be implemented using hardware, software, firmware, and/orany combination thereof. In some embodiments, system 400 may be embodiedas an IED, while in other embodiments, certain components or functionsdescribed herein may be associated with other devices or performed byother devices. The specifically illustrated configuration is merelyrepresentative of one embodiment consistent with the present disclosure.

System 400 includes a communication subsystem 432 to communicate withdevices and/or IEDs. In certain embodiments, communication subsystem 432may facilitate direct communication with other IEDs or communicate withsystems over a communications network. Measurements relating toelectrical conditions and other information used by system 400 may betransmitted via communication subsystem 432. Further, measurements andinformation created by system 400 may be transmitted via communicationsubsystem 432 to other components.

A monitored equipment interface 430 may receive status information from,and issue control instructions to, a piece of monitored equipment (suchas a generator, transformer, circuit breaker, or the like). Monitoredequipment interface 430 may implement control actions upon the detectionof an over-excitation condition. Such instructions may include changingan excitation of a generator or a transformer or disconnecting agenerator or a transformer.

Processor 424 processes communications received via communicationsubsystem 432, monitored equipment interface 430, and the othersubsystems and components in system 400. Processor 424 may operate usingany number of processing rates and architectures. Processor 424 mayperform various algorithms and calculations described herein. Processor424 may be embodied as a general-purpose integrated circuit, anapplication-specific integrated circuit, a field-programmable gatearray, and/or any other suitable programmable logic device. Processor424 may communicate with other elements in system 400 by way of bus 446.

Computer-readable medium 448 may comprise any of a variety ofnon-transitory computer-readable storage media. Computer-readable medium448 may comprise executable instructions to perform processes describedherein. Computer-readable medium 448 may comprise non-transitorymachine-readable media such as, but is not limited to, hard drives,removable media, optical disks, CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs,EEPROMs, magnetic or optical cards, solid-state memory devices, or othertypes of media/machine-readable media suitable for storing electronicinstructions. Such electronic instructions may be executed on processor424.

A sensor subsystem 410 may receive current measurements (I) and/orvoltage measurements (V)The sensor subsystem 410 may comprise A/Dconverters 418 that sample and/or digitize filtered waveforms to formcorresponding digitized current and voltage signals provided to a databus 422. A high-fidelity current transformer 402 and/or a high-fidelityvoltage transformer 414 may include separate signals from each phase ofa three-phase electric power system. ND converters 418 may be connectedto processor 424 by way of data bus 422, through which digitizedrepresentations of current and voltage signals may be transmitted toprocessor 424.

System 400 may further comprise a time input 412, which may be used toreceive a time signal (e.g., a common time reference) allowing system400 to apply a time-stamp to the acquired samples. In variousembodiments, the common time reference may comprise a time signalderived from a GNSS. In certain embodiments, a common time reference maybe received via communications subsystem 432, and accordingly, aseparate time input 412 may not be required for time-stamping and/orsynchronization operations. One such embodiment may employ the IEEE 1588protocol.

Data acquisition subsystem 434 may collect data samples such as thecurrent and voltage measurements. The data samples may be associatedwith a timestamp and made available for retrieval and/or transmission toa remote IED via communication subsystem 432. Data acquisition subsystem434 may operate in conjunction with fault detection subsystem 436. Dataacquisition subsystem 434 may control the recording of data used by thefault detection subsystem 436. According to one embodiment, dataacquisition subsystem 434 may selectively store and retrieve data andmay make the data available for further processing. Such processing mayinclude processing by fault detection subsystem 436, which may beconfigured to determine the occurrence of a fault within an electricpower distribution system.

Traveling wave subsystem 438 may operate in conjunction with dataacquisition subsystem 434 to measure and record traveling waves inreal-time since they are transient signals that dissipate rapidly in anelectric power delivery system. Traveling waves may also be analyzed inconjunction with fault detection subsystem 436 to identify theoccurrence of a fault and the location of the fault.

A protective action subsystem 440 may implement a protective actionbased on the identification of a fault by fault detection subsystem 436.In various embodiments, a protective action may include tripping abreaker, selectively isolating a portion of the electric power system,etc. Protective action subsystem 440 may coordinate protective actionswith other devices in communication with system 400.

A linear prediction subsystem 442 may encode and compress data to bestored on computer-readable medium 448 and or decode and decompress dataretrieved from computer-readable medium 448. Linear prediction subsystem442 may generate an excitation signal estimate, linear predictioncoefficients, and an estimated signal. In one specific embodiment,linear prediction subsystem 442 may embody linear prediction estimator202 and linear predictor 204 illustrated in FIG. 2A and linear predictor304 illustrated in FIG. 3.

A difference encoder subsystem 444 may provide efficient coding of aninput stream representing an error between an input signal and anestimated signal generated by linear prediction subsystem 442. Further,difference encoder subsystem 444 may decode an encoding of the errorsignal stored on computer-readable medium 448. Alternatively, differenceencoder subsystem 444 may decode an encoding of the error signalreceived via communications subsystem 432. In one specific embodiment,difference encoder subsystem 444 may embody Golomb coder 210 illustratedin FIG. 2A and Golomb decoder 306 illustrated in FIG. 3. In otherembodiments, difference encoder subsystem 444 may be configured toencode the error signal using other techniques, including a zigzagencoding scheme and/or a variable-length encoding scheme.

In various embodiments, linear prediction subsystem 442 and differenceencoder subsystem 444 may operate at speeds sufficient to supportreal-time encoding and compression of data collected by system 400. Inone embodiment, system 400 may allow for encoding and compression of onemillion samples per second in addition to providing protectionfunctionality.

FIG. 5 illustrates a flow chart of a method 500 to encode power systemsignals using linear prediction and Golomb codes consistent withembodiments of the present disclosure. At 502, a system may receive aninput signal comprising a plurality of high-speed representations ofelectrical conditions in an electric power system. Such conditions maybe current measurements, voltage measurements, or other types ofmeasurements.

At 504, an excitation signal estimate may be generated based on theinput signal. The excitation signal estimate may be pitched orunpitched, depending on the results of a frequency estimation technique,such as autocorrelation.

At 506, a plurality of linear prediction coefficients may be generatedbased on the input signal. The linear prediction coefficients mayrepresent the rate of change or slope of a portion of the input signal.In one specific embodiment, the excitation signal and the plurality oflinear prediction coefficients may be generated by a linear predictionestimator, such as linear prediction estimator 202 illustrated in FIG.2A.

At 508, an estimated signal may be generated based on the excitationsignal and the plurality of linear prediction coefficients. A linearpredictor, such as linear predictor 204 illustrated in FIG. 2A, maygenerate the estimated signal. In some embodiments, a linear predictionsubsystem, such as linear prediction subsystem 442 illustrated in FIG.4, may comprise a linear prediction estimator and a linear predictor.

At 510, an error signal may be determined based on a difference betweenthe input signal and the estimated signal. The error signal may bedetermined by determining a difference between the input signal and theestimated signal. In one embodiment, the difference may be determinedusing summer 208 illustrated in FIG. 2A, or the error signal may bedetermined by a processor, such as processor 424 illustrated in FIG. 4.

At 512, a plurality of Golomb codes may be generated to represent theerror signal. The plurality of Golomb codes may each comprise a quotientand a remainder determined by an error value divided by a tunableparameter. In some embodiments, the quotient is encoded using unaryencoding and the remainder is encoded using truncated binary encoding.

At 514, the excitation signal, the plurality of linear predictioncoefficients, and the plurality of Golomb codes may be stored on acomputer-readable storage medium. In some embodiments, a compressionratio of the input signal and the encoded and compressed representationof the input signal is between approximately 4 and 8. The compressionratio may depend on various aspects of the input signal. In otherembodiments, the compression ratio may be less than 4 or greater than 8.

While specific embodiments and applications of the disclosure have beenillustrated and described, it is to be understood that the disclosure isnot limited to the precise configurations and components disclosedherein. Accordingly, many changes may be made to the details of theabove-described embodiments without departing from the underlyingprinciples of this disclosure. The scope of the present inventionshould, therefore, be determined only by the following claims.

What is claimed is:
 1. A system to encode and compress an input signalrepresenting a parameter in an electric power system using linearprediction and Golomb codes, comprising: a data acquisition subsystem toreceive an input signal comprising a plurality of high-speedrepresentations of electrical conditions associated with at least aportion of the electric power system; a linear prediction subsystem togenerate: an excitation signal estimate based on the input signal; aplurality of linear prediction coefficients based on the input signal;and an estimated signal based on the excitation signal estimate and theplurality of linear prediction coefficients; a Golomb subsystem togenerate a plurality of Golomb codes to represent an error signal basedon a difference between the input signal and the estimated signal; and anon-transitory computer-readable storage medium to store an encoded andcompressed representation of the input signal comprising the excitationsignal estimate, the plurality of linear prediction coefficients, andthe plurality of Golomb codes.
 2. The system of claim 1, wherein theinput signal comprises about one million samples per second and thesystem is configured to encode and compress the input signal in realtime.
 3. The system of claim 1, wherein the system is further configuredto decode and decompress the input signal stored on the non-transitorycomputer-readable storage medium.
 4. The system of claim 1, furthercomprising a communication subsystem to transmit the encoded andcompressed representation of the input signal.
 5. The system of claim 1,wherein each of the plurality of Golomb codes comprises a quotient and aremainder determined by an error value divided by a tunable parameter.6. The system of claim 5, wherein the quotient is encoded using unaryencoding.
 7. The system of claim 5, wherein the remainder is encodedusing truncated binary encoding.
 8. The system of claim 1, wherein acompression ratio of the input signal and the encoded and compressedrepresentation of the input signal is between approximately 4 and
 8. 9.The system of claim 1, further comprising a fault detection subsystem todetect a fault in the electric power system based on the input signal.10. The system of claim 9, further comprising a protective actionsubsystem to implement a protective action based on detection of thefault.
 11. A method of encoding and compressing an input signalrepresenting a parameter in an electric power system using linearprediction and Golomb codes, comprising: receiving, using a dataacquisition subsystem, an input signal comprising a plurality ofhigh-speed representations of electrical conditions associated with atleast a portion of the electric power system; generating, using a linearprediction subsystem, an excitation signal estimate based on the inputsignal; generating, using the linear prediction subsystem, a pluralityof linear prediction coefficients based on the input signal; generating,using the linear prediction subsystem, an estimated signal based on theexcitation signal estimate and the plurality of linear predictioncoefficients; generating, using a Golomb subsystem, a plurality ofGolomb codes to represent an error signal based on a difference betweenthe input signal and the estimated signal; and storing, using anon-transitory computer-readable storage medium, an encoded andcompressed representation of the input signal comprising the excitationsignal estimate, the plurality of linear prediction coefficients, andthe plurality of Golomb codes.
 12. The method of claim 11, wherein theinput signal comprises about one million samples per second and whereinencoding and compressing the input signal occurs in real time.
 13. Themethod of claim 11, further comprising decoding and decompressing theinput signal stored on the non-transitory computer-readable storagemedium.
 14. The method of claim 11, further comprising transmitting,using a communication subsystem, the encoded and compressedrepresentation of the input signal.
 15. The method of claim 11, whereineach of the plurality of Golomb codes comprises a quotient and aremainder determined by an error value divided by a tunable parameter.16. The method of claim 15, further comprising encoding the quotientusing unary encoding.
 17. The method of claim 15, further comprisingencoding the remainder using truncated binary encoding.
 18. The methodof claim 11, wherein a compression ratio of the input signal and theencoded and compressed representation of the input signal is betweenapproximately 4 and
 8. 19. The method of claim 11, further comprisingdetecting, using a fault detection subsystem, a fault in the electricpower system based on the input signal.
 20. The method of claim 19,further comprising implementing, using a protective action subsystem, aprotective action based on detection of the fault.
 21. A system toencode and compress an input signal in real time representing aparameter in an electric power system using linear prediction and anencoded error signal, comprising: a data acquisition subsystem toreceive an input signal comprising a plurality of high-speedrepresentations of electrical conditions associated with at least aportion of the electric power system; a linear prediction subsystem togenerate a linear estimated signal using a plurality of fixedcoefficients; an error signal subsystem to generate an encoded errorsignal to represent an error between the input signal and the linearestimated signal; and a communication interface to transmit theestimated signal and the encoded error signal to a receiving device. 22.The system of claim 21, wherein the error signal subsystem is configuredto encode the encoded error signal using a zigzag encoding scheme. 23.The system of claim 21, wherein the error signal subsystem is configuredto encode the encoded error signal using a variable-length encodingscheme.